Data profile for this category
It typically serves cotton spinning, chemical fiber spinning, woven, and knitting enterprises. Daily generated documents include cotton blending or batching lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, defect records, and more. Data sources include raw fiber inspection results, ERP systems, real-time data from spinning and weaving machines, evenness testers, fabric inspection machine collected data, and order specification parameters. Updates sync with each batch of raw materials, each shift’s production, and each piece of fabric output. Fields cover fiber indicators, equipment operation parameters, inspection results, defect types and locations, etc. Units are adapted to the corresponding business links.
Constraints imposed by these characteristics on workflow orchestration
Multi-source data access requires the workflow to be configured with multiple types of input nodes, each connecting to ERP systems, equipment sensors, and inspection systems separately. Frequently updated production data requires the workflow to support triggering execution by production batch, shift, or number of fabric pieces, to avoid data lag or duplicate summaries. Diverse document formats require the workflow to preset different format parsing rules, adapting to text parameters from cotton blending lists, numerical data from equipment records, and structured results from inspection reports. Large fluctuations in data volume from multiple equipment units require the workflow to be configured with adjustable parallel processing thresholds, to meet production ledger summary needs of different scales.
Configuration settings
This configuration is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Rule | Trigger by production batch, shift, or number of fabric pieces | Match data update frequency to avoid lag or duplicate summaries |
| Input Node Configuration | Connect to 3 independent ports for ERP, evenness tester, and fabric inspection machine separately | Adapt to multi-source data access requirements |
| Timeout Threshold | 600 seconds | Adapt to delay fluctuations in equipment data collection |
variables binding | Associate inspection results, equipment parameters, and order specification fields | Ensure accurate data transfer and reference within the workflow |
| Error Retry Count | 3 times | Address abnormal scenarios of temporary equipment data interruption |
nodeId configuration | Bind the unique identifier of the ledger summary and report generation nodes | Ensure accurate workflow node calls |
Three common errors
- Code running results cannot be passed to AI models in the workflow, and an error
chat:ai_input_is_eis prompted during AI calls. The cause is that the output result of the code node is not bound to the AI input parameter via thevariablesconfiguration item, causing the model to fail to obtain valid input content. - When using the database connection plugin to connect to PostgreSQL, workflow verification fails, and the interface prompts "Workflow verification failed, please check for missing or empty values, and whether connections are properly wired". The cause is that required configuration items for database connection are not filled correctly, or data flow connections between nodes do not cover the complete link, causing the system to fail to recognize valid connection configurations.
- Knowledge base variable reference format is incorrect, making it impossible to call the corresponding dataset normally. The cause is that the standard variable reference rules are not followed, and non-standard format templates are used incorrectly, causing the system to fail to recognize the target content corresponding to the variable.
How to verify correct configuration
- Trigger the workflow once, check whether each input node successfully pulls the latest data from the corresponding system, and verify that the data fields match the business documents.
- Check whether the parameters bound by
variablescover all business fields that need to be transferred, and confirm that the output of the code node or data node has been correctly associated with subsequent processing nodes. - Run the workflow and view the verification log, confirm that there are no errors in database connections and node calls, and that the trigger rule matches the business rhythm of update frequency.
- View the finally generated ledger report, confirm that parameters, inspection results, and defect records of all business links have been correctly summarized, and the format meets preset requirements.
The values provided are common starting points and should be measured against the reader's own samples.